Key result
Nonlinear feature-based classification of pain-ERP using correlation dimension with moving variance achieved 100% accuracy for two- and three-level pain perception classification.
Why the study?
Previous studies using pain-ERP to evaluate pain perception failed to achieve high accuracy in estimating multiple pain levels, mainly due to a lack of investigation into feature extraction.
Does nonlinear feature-based classification of pain-ERP improve the accuracy of assessing multiple pain perception levels in healthy subjects?
Does nonlinear feature-based classification of pain-ERP improve the accuracy of assessing multiple pain perception levels in healthy subjects?
Nonlinear feature-based classification of pain-ERP shows potential for objective pain assessment, achieving high accuracy for two- and three-level pain perception classification.
Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi's fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.
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Tripanpitak et al. (2020) studied Pain perception. Nonlinear feature-based classification of pain-ERP was evaluated on Accuracy of pain perception level classification. Nonlinear feature-based classification of pain-ERP using correlation dimension with moving variance achieved 100% accuracy for two- and three-level pain perception classification.
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